{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Linear Regression with Multiple Variables.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "ULEFYV-wQ6O1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt \n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "from sklearn import datasets,linear_model, metrics "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NLrwxpzQQ6O6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "boston = datasets.load_boston()\n",
        "X = boston.data \n",
        "y = boston.target\n",
        "# X=pd.DataFrame(X)\n",
        "# print(X)\n",
        "# y=pd.DataFrame(y)\n",
        "# print(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "twdZA2WXQ6PC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4,random_state=1) \n",
        "# print(X_train.shape)\n",
        "# print(y_train.shape)\n",
        "# print(X_test.shape)\n",
        "# print(y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SHL24reTQ6PF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "1b2387e9-b960-4dab-82b8-b588228556a6"
      },
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()\n",
        "reg.fit(X_train, y_train)\n",
        "print('Coefficients: \\n', reg.coef_) "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: \n",
            " [-8.80740828e-02  6.72507352e-02  5.10280463e-02  2.18879172e+00\n",
            " -1.72283734e+01  3.62985243e+00  2.13933641e-03 -1.36531300e+00\n",
            "  2.88788067e-01 -1.22618657e-02 -8.36014969e-01  9.53058061e-03\n",
            " -5.05036163e-01]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eBKVS3E1Q6PJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e1caad54-e25e-47ed-9547-34be3ce1ccc1"
      },
      "cell_type": "code",
      "source": [
        "print('Variance score: ',reg.score(X_test, y_test))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variance score:  0.7208987846108872\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fRHoTmzDQ6PN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "83c84676-5753-4d89-acc5-1ebfffd1035a"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, \n",
        "            color = \"green\", s = 10, label = 'Train data') \n",
        "  \n",
        " \n",
        "plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, \n",
        "            color = \"blue\", s = 10, label = 'Test data') \n",
        "  \n",
        " \n",
        "plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) \n",
        "\n",
        "plt.legend(loc = 'upper right')  \n",
        "plt.title(\"Residual errors\") \n",
        "plt.show() "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mPPr2RmT3GeI6o1Or81Bjxw4BpaVRyM1tap3L2qyxZqGqVCEi3QzkNQDRLf88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\nC1lERMQCvgMuzYqNM8XwqAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc2cf65d860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nhfQucBQQ6PR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "8b441877-f84c-447d-a44c-3e4ccf860f47"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(reg.predict(X_train),y_train,label=\"Train data\")\n",
        "plt.scatter(reg.predict(X_test),y_test, color = \"g\",label=\"Test data\") \n",
        "plt.legend(loc = 'lower right')  \n",
        "plt.title(\"Test Vs Train\") \n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pWSwd94S/YhMLDstfDy26AAAqvh8S1EbY2PqQ57ISc4Jq+Uazj0jn0Qo7IUQR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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc2d0ff2c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-2_JixgKQ6PU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}